A recurrent neural fuzzy controller based on self‐organizing improved particle swarm optimization for a magnetic levitation system

DOI10.1002/ACS.2489zbMath1330.93146OpenAlexW1502132803MaRDI QIDQ5743784

Cheng-Jian Lin, Cheng-Hung Chen

Publication date: 8 February 2016

Published in: International Journal of Adaptive Control and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/acs.2489

zbMATH Keywords

learning systemintelligent controlparticle swarm optimizationmagnetic levitation systemsneural fuzzy controller

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Fuzzy control/observation systems (93C42) Neural networks for/in biological studies, artificial life and related topics (92B20) Application models in control theory (93C95)

Related Items (1)

Retracted: SCRIM drive system using adaptive backstepping control and mended recurrent Romanovski polynomials neural network with reformed particle swarm optimization

Cites Work

• New dynamic fuzzy structure and dynamic system identification
• A self-constructing fuzzy CMAC model and its applications
• A hybrid evolutionary learning algorithm for TSK-type fuzzy model design
• Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation
• Extended fuzzy function model with stable learning methods for online system identification
• New evolutionary direction operator for genetic algorithms
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